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DA2F: Research on Robustness of Deep Learning Models Using ..:
, In:
2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)
,
Bian, Yuqi
;
Wang, Jing
;
Zhang, Weigong
. - p. 412-419 , 2022
Link:
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57..
RT T1 <cTitle>2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)</cTitle>: T1 <Title>DA2F: Research on Robustness of Deep Learning Models Using Approximate Activation Function</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10074931&Exemplar=1&LAN=DE A1 Bian, Yuqi A1 Wang, Jing A1 Zhang, Weigong A1 Gao, Lan YR 2022 K1 Deep learning K1 Costs K1 Computational modeling K1 Piecewise linear approximation K1 Computer architecture K1 Robustness K1 Safety K1 Deep learning models K1 approximate computing K1 adversarial example K1 model robustness SP 412 OP 419 LK http://dx.doi.org/https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00084 DO https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00084 SF ELIB - SuUB Bremen
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